OpenRaiser/Intern-Atlas huggingface dataset:
Weak Cost Register Automata are Still Powerful
Venue: arXiv cs.FL 2018
Year: 2018

We consider one of the weakest variants of cost register automata over a tropical semiring, namely copyless cost register automata over $\mathbb{N}$ with updates using $\min$ and increments. We show that this model can simulate, in some sense, the runs of counter machines with zero-tests. We deduce that a number of problems pertaining to that model are undecidable, in particular equivalence, disproving a conjecture of Alur et al. from 2012. To emphasize how weak these machines are, we also show that they can be expressed as a restricted form of linearly-ambiguous weighted automata.
High Dimensional Time Series Generators
Venue: arXiv cs.LG 2018
Year: 2018

Multidimensional time series are sequences of real valued vectors. They occur in different areas, for example handwritten characters, GPS tracking, and gestures of modern virtual reality motion controllers. Within these areas, a common task is to search for similar time series. Dynamic Time Warping (DTW) is a common distance function to compare two time series. The Edit Distance with Real Penalty (ERP) and the Dog Keeper Distance (DK) are two more distance functions on time series. Their behaviour has been analyzed on 1-dimensional time series. However, it is not easy to evaluate their behaviour in relation to growing dimensionality. For this reason we propose two new data synthesizers generating multidimensional time series. The first synthesizer extends the well known cylinder-bell-funnel (CBF) dataset to multidimensional time series. Here, each time series has an arbitrary type (cylinder, bell, or funnel) in each dimension, thus for $d$-dimensional time series there are $3^{d}$ different classes. The second synthesizer (RAM) creates time series with ideas adapted from Brownian motions which is a common model of movement in physics. Finally, we evaluate the applicability of a 1-nearest neighbor classifier using DTW on datasets generated by our synthesizers.
Im2Avatar: Colorful 3D Reconstruction from a Single Image
Venue: arXiv cs.CV 2018
Year: 2018

Existing works on single-image 3D reconstruction mainly focus on shape recovery. In this work, we study a new problem, that is, simultaneously recovering 3D shape and surface color from a single image, namely "colorful 3D reconstruction". This problem is both challenging and intriguing because the ability to infer textured 3D model from a single image is at the core of visual understanding. Here, we propose an end-to-end trainable framework, Colorful Voxel Network (CVN), to tackle this problem. Conditioned on a single 2D input, CVN learns to decompose shape and surface color information of a 3D object into a 3D shape branch and a surface color branch, respectively. Specifically, for the shape recovery, we generate a shape volume with the state of its voxels indicating occupancy. For the surface color recovery, we combine the strength of appearance hallucination and geometric projection by concurrently learning a regressed color volume and a 2D-to-3D flow volume, which are then fused into a blended color volume. The final textured 3D model is obtained by sampling color from the blended color volume at the positions of occupied voxels in the shape volume. To handle the severe sparse volume representations, a novel loss function, Mean Squared False Cross-Entropy Loss (MSFCEL), is designed. Extensive experiments demonstrate that our approach achieves significant improvement over baselines, and shows great generalization across diverse object categories and arbitrary viewpoints.
Modelling Bitcoin in Agda
Venue: arXiv cs.CR 2018
Year: 2018

We present two models of the block chain of Bitcoin in the interactive theorem prover Agda. The first one is based on a simple model of bank accounts, while having transactions with multiple inputs and outputs. The second model models transactions, which refer directly to unspent transaction outputs, rather than user accounts. The resulting blockchain gives rise to a transaction tree. That model is formalised using an extended form of induction-recursion, one of the unique features of Agda. The set of transaction trees and transactions is defined inductively, while simultaneously recursively defining the list of unspent transaction outputs. Both structures model standard transactions, coinbase transactions, transaction fees, the exact message to be signed by those spending money in a transaction, block rewards, blocks, and the blockchain, and the second structure models as well maturation time for coinbase transactions and Merkle trees. Hashing and cryptographic operations and their correctness are dealt with abstractly by postulating corresponding operations. An indication is given how the correctness of this model could be specified and proven in Agda.
The emergent integrated network structure of scientific research
Venue: arXiv cs.SI 2018
Year: 2018

The practice of scientific research is often thought of as individuals and small teams striving for disciplinary advances. Yet as a whole, this endeavor more closely resembles a complex system of natural computation, in which information is obtained, generated, and disseminated more effectively than would be possible by individuals acting in isolation. Currently, the structure of this integrated and innovative landscape of scientific ideas is not well understood. Here we use tools from network science to map the landscape of interconnected research topics covered in the multidisciplinary journal PNAS since 2000. We construct networks in which nodes represent topics of study and edges give the degree to which topics occur in the same papers. The network displays small-world architecture, with dense connectivity within scientific clusters and sparse connectivity between clusters. Notably, clusters tend not to align with assigned article classifications, but instead contain topics from various disciplines. Using a temporal graph, we find that small-worldness has increased over time, suggesting growing efficiency and integration of ideas. Finally, we define a novel measure of interdisciplinarity, which is positively associated with PNAS's impact factor. Broadly, this work suggests that complex and dynamic patterns of knowledge emerge from scientific research, and that structures reflecting intellectual integration may be beneficial for obtaining scientific insight.
Efficient Search of Compact QC-LDPC and SC-LDPC Convolutional Codes with Large Girth
Venue: arXiv cs.IT 2018
Year: 2018

We propose a low-complexity method to find quasi-cyclic low-density parity-check block codes with girth 10 or 12 and shorter length than those designed through classical approaches. The method is extended to time-invariant spatially coupled low-density parity-check convolutional codes, permitting to achieve small syndrome former constraint lengths. Several numerical examples are given to show its effectiveness.
Towards Lightweight Error Detection Schemes for Implementations of MixColumns in Lightweight Cryptography
Venue: arXiv cs.CR 2018
Year: 2018

In this paper, through considering lightweight cryptography, we present a comparative realization of MDS matrices used in the VLSI implementations of lightweight cryptography. We verify the MixColumn/MixNibble transformation using MDS matrices and propose reliability approaches for thwarting natural and malicious faults. We note that one other contribution of this work is to consider not only linear error detecting codes but also recomputation mechanisms as well as fault space transformation (FST) adoption for lightweight cryptographic algorithms. Our intention in this paper is to propose reliability and error detection mechanisms (through linear codes, recomputations, and FST adopted for lightweight cryptography) to consider the error detection schemes in designing beforehand taking into account such algorithmic security. We also posit that the MDS matrices applied in the MixColumn (or MixNibble) transformation of ciphers to protect ciphers against linear and differential attacks should be incorporated in the cipher design in order to reduce the overhead of the applied error detection schemes. Finally, we present a comparative implementation framework on ASIC to benchmark the VLSI hardware implementation presented in this paper.
Numerical Integration in Multiple Dimensions with Designed Quadrature
Venue: arXiv cs.NA 2018
Year: 2018

We present a systematic computational framework for generating positive quadrature rules in multiple dimensions on general geometries. A direct moment-matching formulation that enforces exact integration on polynomial subspaces yields nonlinear conditions and geometric constraints on nodes and weights. We use penalty methods to address the geometric constraints, and subsequently solve a quadratic minimization problem via the Gauss-Newton method. Our analysis provides guidance on requisite sizes of quadrature rules for a given polynomial subspace, and furnishes useful user-end stability bounds on error in the quadrature rule in the case when the polynomial moment conditions are violated by a small amount due to, e.g., finite precision limitations or stagnation of the optimization procedure. We present several numerical examples investigating optimal low-degree quadrature rules, Lebesgue constants, and 100-dimensional quadrature. Our capstone examples compare our quadrature approach to popular alternatives, such as sparse grids and quasi-Monte Carlo methods, for problems in linear elasticity and topology optimization.
Smart Conversational Agents for Reminiscence
Venue: arXiv cs.HC 2018
Year: 2018

In this paper we describe the requirements and early system design for a smart conversational agent that can assist older adults in the reminiscence process. The practice of reminiscence has well documented benefits for the mental, social and emotional well-being of older adults. However, the technology support, valuable in many different ways, is still limited in terms of need of co-located human presence, data collection capabilities, and ability to support sustained engagement, thus missing key opportunities to improve care practices, facilitate social interactions, and bring the reminiscence practice closer to those with less opportunities to engage in co-located sessions with a (trained) companion. We discuss conversational agents and cognitive services as the platform for building the next generation of reminiscence applications, and introduce the concept application of a smart reminiscence agent.
Combating the Control Signal Spoofing Attack in UAV Systems
Venue: arXiv cs.IT 2018
Year: 2018

Unmanned aerial vehicle (UAV) system is vulnerable to the control signal spoofing attack due to the openness of the wireless communications. In this correspondence, a physical layer approach is proposed to combat the control signal spoofing attack, i.e,. to determine whether the received control signal packet is from the ground control station (GCS) or a potential malicious attacker (MA), which does not need to share any secret key. We consider the worst case where the UAV does not have any prior knowledge about the MA. Utilizing the channel feature of the angles of arrival, the distance-based path loss, and the Rician-$κ$ factor, we construct a generalized log-likelihood radio (GLLR) test framework to handle the problem. Accurate approximations of the false alarm and successful detection rate are provided to efficiently evaluate the performance.
On the Completeness of Verifying Message Passing Programs under Bounded Asynchrony
Venue: arXiv cs.PL 2018
Year: 2018

We address the problem of verifying message passing programs, defined as a set of parallel processes communicating through unbounded FIFO buffers. We introduce a bounded analysis that explores a special type of computations, called k-synchronous. These computations can be viewed as (unbounded) sequences of interaction phases, each phase allowing at most k send actions (by different processes), followed by a sequence of receives corresponding to sends in the same phase. We give a procedure for deciding k-synchronizability of a program, i.e., whether every computation is equivalent (has the same happens-before relation) to one of its k-synchronous computations. We also show that reachability over k-synchronous computations and checking k-synchronizability are both PSPACE-complete. Furthermore, we introduce a class of programs called {\em flow-bounded} for which the problem of deciding whether there exists a k>0 for which the program is k-synchronizable, is decidable.
Considerations Regarding the Modelling of Wind Energy Conversion Systems
Venue: arXiv cs.SY 2018
Year: 2018

The above paper presents some considerations regarding the modelling of wind energy conversion systems (WECS). There are presented practical problems of grid integration of wind turbines, the usage of general system models, respectively of RMS (root mean squares) models. There are presented models of some WECS components and related, such as: a probabilistic 2D model for instantaneous wind velocities, aerodynamic model of wind turbine, rotating inertia model, asynchronous machine model, grid model. This paper only presents models used in different WECS, models that can be easily simulated with adequate simulation tools such as Matlab-Simulink.
NTUA-SLP at SemEval-2018 Task 2: Predicting Emojis using RNNs with Context-aware Attention
Venue: arXiv cs.CL 2018
Year: 2018

In this paper we present a deep-learning model that competed at SemEval-2018 Task 2 "Multilingual Emoji Prediction". We participated in subtask A, in which we are called to predict the most likely associated emoji in English tweets. The proposed architecture relies on a Long Short-Term Memory network, augmented with an attention mechanism, that conditions the weight of each word, on a "context vector" which is taken as the aggregation of a tweet's meaning. Moreover, we initialize the embedding layer of our model, with word2vec word embeddings, pretrained on a dataset of 550 million English tweets. Finally, our model does not rely on hand-crafted features or lexicons and is trained end-to-end with back-propagation. We ranked 2nd out of 48 teams.
NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer Learning
Venue: arXiv cs.CL 2018
Year: 2018

In this paper we present deep-learning models that submitted to the SemEval-2018 Task~1 competition: "Affect in Tweets". We participated in all subtasks for English tweets. We propose a Bi-LSTM architecture equipped with a multi-layer self attention mechanism. The attention mechanism improves the model performance and allows us to identify salient words in tweets, as well as gain insight into the models making them more interpretable. Our model utilizes a set of word2vec word embeddings trained on a large collection of 550 million Twitter messages, augmented by a set of word affective features. Due to the limited amount of task-specific training data, we opted for a transfer learning approach by pretraining the Bi-LSTMs on the dataset of Semeval 2017, Task 4A. The proposed approach ranked 1st in Subtask E "Multi-Label Emotion Classification", 2nd in Subtask A "Emotion Intensity Regression" and achieved competitive results in other subtasks.
NTUA-SLP at SemEval-2018 Task 3: Tracking Ironic Tweets using Ensembles of Word and Character Level Attentive RNNs
Venue: arXiv cs.CL 2018
Year: 2018

In this paper we present two deep-learning systems that competed at SemEval-2018 Task 3 "Irony detection in English tweets". We design and ensemble two independent models, based on recurrent neural networks (Bi-LSTM), which operate at the word and character level, in order to capture both the semantic and syntactic information in tweets. Our models are augmented with a self-attention mechanism, in order to identify the most informative words. The embedding layer of our word-level model is initialized with word2vec word embeddings, pretrained on a collection of 550 million English tweets. We did not utilize any handcrafted features, lexicons or external datasets as prior information and our models are trained end-to-end using back propagation on constrained data. Furthermore, we provide visualizations of tweets with annotations for the salient tokens of the attention layer that can help to interpret the inner workings of the proposed models. We ranked 2nd out of 42 teams in Subtask A and 2nd out of 31 teams in Subtask B. However, post-task-completion enhancements of our models achieve state-of-the-art results ranking 1st for both subtasks.
A Robot to Shape your Natural Plant: The Machine Learning Approach to Model and Control Bio-Hybrid Systems
Venue: arXiv cs.NE 2018
Year: 2018

Bio-hybrid systems---close couplings of natural organisms with technology---are high potential and still underexplored. In existing work, robots have mostly influenced group behaviors of animals. We explore the possibilities of mixing robots with natural plants, merging useful attributes. Significant synergies arise by combining the plants' ability to efficiently produce shaped material and the robots' ability to extend sensing and decision-making behaviors. However, programming robots to control plant motion and shape requires good knowledge of complex plant behaviors. Therefore, we use machine learning to create a holistic plant model and evolve robot controllers. As a benchmark task we choose obstacle avoidance. We use computer vision to construct a model of plant stem stiffening and motion dynamics by training an LSTM network. The LSTM network acts as a forward model predicting change in the plant, driving the evolution of neural network robot controllers. The evolved controllers augment the plants' natural light-finding and tissue-stiffening behaviors to avoid obstacles and grow desired shapes. We successfully verify the robot controllers and bio-hybrid behavior in reality, with a physical setup and actual plants.
Modelling Evaluation of MPLS using Physical and virtual Network on GNS3
Venue: arXiv cs.NI 2018
Year: 2018

The Multi-Protocol Label Switching (MPLS) is an emerging technology which have quality, effectiveness and administration quality. On the contrary, traditional network inside part passage steering conventions ruin the viable acknowledgment of modern activity designing approaches in legacy IP systems. Virtualization of the network could easily be assuring the network performance and virtual network are logically connected with one physical machine so that data could easily be send and get information from one virtual machine to the next machine. The purpose of this Paper is to analyse the traffic of MPLS using physical and virtual networks. This Paper will show that MPLS could also be run on physical and virtual networks. MPLS is running nowadays to provide local area network speed into the wide area Network.
Index Codes for Interlinked Cycle Structures with Outer Cycles
Venue: arXiv cs.IT 2018
Year: 2018

Index code construction for a class of side-information graphs called interlinked cycle (IC) structures without outer cycles is given by Thapa, Ong and Johnson (C. Thapa, L. Ong, and S. Johnson, "Interlinked Cycles for Index Coding: Generalizing Cycles and Cliques", in \textit{IEEE Trans. Inf. Theory, vol. 63, no. 6, Jun. 2017}, "Interlinked Cycles for Index Coding: Generalizing Cycles and Cliques", in arxiv (arxiv:1603.00092v2 [cs.IT] 25 Feb 2018)). In this paper, construction of index codes for interlinked cycle (IC) structures with outer cycles is given along with a decoding algorithm.
Automated detection of vulnerable plaque in intravascular ultrasound images
Venue: arXiv cs.CV 2018
Year: 2018

Acute Coronary Syndrome (ACS) is a syndrome caused by a decrease in blood flow in the coronary arteries. The ACS is usually related to coronary thrombosis and is primarily caused by plaque rupture followed by plaque erosion and calcified nodule. Thin-cap fibroatheroma (TCFA) is known to be the most similar lesion morphologically to a plaque rupture. In this paper, we propose methods to classify TCFA using various machine learning classifiers including Feed-forward Neural Network (FNN), K-Nearest Neighbor (KNN), Random Forest (RF) and Convolutional Neural Network (CNN) to figure out a classifier that shows optimal TCFA classification accuracy. In addition, we suggest pixel range based feature extraction method to extract the ratio of pixels in the different region of interests to reflect the physician's TCFA discrimination criteria. A total of 12,325 IVUS images were labeled with corresponding OCT images to train and evaluate the classifiers. We achieved 0.884, 0.890, 0.878 and 0.933 Area Under the ROC Curve (AUC) in the order of using FNN, KNN, RF and CNN classifier. As a result, the CNN classifier performed best and the top 10 features of the feature-based classifiers (FNN, KNN, RF) were found to be similar to the physician's TCFA diagnostic criteria.
Modeling and Analysis of Leaky Deception using Signaling Games with Evidence
Venue: arXiv cs.CR 2018
Year: 2018

Deception plays critical roles in economics and technology, especially in emerging interactions in cyberspace. Holistic models of deception are needed in order to analyze interactions and to design mechanisms that improve them. Game theory provides such models. In particular, existing work models deception using signaling games. But signaling games inherently model deception that is undetectable. In this paper, we extend signaling games by including a detector that gives off probabilistic warnings when the sender acts deceptively. Then we derive pooling and partially-separating equilibria of the game. We find that 1) high-quality detectors eliminate some pure-strategy equilibria, 2) detectors with high true-positive rates encourage more honest signaling than detectors with low false-positive rates, 3) receivers obtain optimal outcomes for equal-error-rate detectors, and 4) surprisingly, deceptive senders sometimes benefit from highly accurate deception detectors. We illustrate these results with an application to defensive deception for network security. Our results provide a quantitative and rigorous analysis of the fundamental aspects of detectable deception.
Computing FO-Rewritings in EL in Practice: from Atomic to Conjunctive Queries
Venue: arXiv cs.AI 2018
Year: 2018

A prominent approach to implementing ontology-mediated queries (OMQs) is to rewrite into a first-order query, which is then executed using a conventional SQL database system. We consider the case where the ontology is formulated in the description logic EL and the actual query is a conjunctive query and show that rewritings of such OMQs can be efficiently computed in practice, in a sound and complete way. Our approach combines a reduction with a decomposed backwards chaining algorithm for OMQs that are based on the simpler atomic queries, also illuminating the relationship between first-order rewritings of OMQs based on conjunctive and on atomic queries. Experiments with real-world ontologies show promising results.